scholarly journals Natural language processing in psychiatry: the promises and perils of a transformative approach

2022 ◽  
pp. 1-3
Author(s):  
Neguine Rezaii ◽  
Phillip Wolff ◽  
Bruce H. Price
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
New Technology ◽  
Processing Methods ◽  
Everyday Language ◽  
Transformative Approach

A person's everyday language can indicate patterns of thought and emotion predictive of mental illness. Here, we discuss how natural language processing methods can be used to extract indicators of mental health from language to help address long-standing problems in psychiatry, along with the potential hazards of this new technology.

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

2021 ◽  
Author(s):  
Arash Maghsoudi ◽  
Sara Nowakowski ◽  
Ritwick Agrawal ◽  
Amir Sharafkhaneh ◽  
Sadaf Aram ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Temporal Analysis ◽  
Health Condition ◽  
Additional Stress ◽  
Mental Health Condition ◽  
Twitter Users

BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

2021 ◽  
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

Cascading Workflow of Healthcare Services

2019 ◽  
Vol 1 (1) ◽  
pp. 79-95
Author(s):  
Phillip Osial ◽  
Arnold Kim ◽  
Kalle Kauranen
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
New Technology ◽  
Healthcare Services ◽  
Discharge Summary ◽  
Healthcare Industry ◽  
Clinical Workflow ◽  
The Us ◽  
Processing Techniques

Despite rapid advancements in technology, the healthcare industry is known to lag behind when it comes to adopting new changes. Most often, when a new technology such as CPOE or EHR systems presents themselves in the healthcare industry, clinicians are left struggling to keep up with their workloads while learning to adjust a new workflow. Instead of disrupting the clinician's clinical workflow, the authors propose a system for transforming clinical narratives presented in the form of discharge summaries from the i2b2 Natural Language Processing dataset into a standardized order set. The proposed system uses natural language processing techniques based on Scala, which extracts discharge summary information about a patient and is proven to be highly scalable. The goal of this system is to increase interoperability between CPOE systems by performing further transformations on the extracted data. The authors adhere to HL7's FHIR standards and use JSON as the primary medical messaging format, which is used both in the US and international healthcare industry organizations and companies.

scholarly journals Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods

2019 ◽  
Vol 2 (8) ◽  
pp. e1910399
Author(s):  
Meliha Skaljic ◽  
Ihsaan H. Patel ◽  
Amelia M. Pellegrini ◽  
Victor M. Castro ◽  
Roy H. Perlis ◽  
...  
Keyword(s):  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Methods

A Proof of Concept for Assessing Emergency Room Use with Primary Care Data and Natural Language Processing

2013 ◽  
Vol 52 (01) ◽  
pp. 33-42 ◽  
Author(s):  
M.-H. Kuo ◽  
P. Gooch ◽  
J. St-Maurice
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Emergency Room ◽  
Language Processing ◽  
Free Text ◽  
Proof Of Concept ◽  
Term Extraction ◽  
Primary Care Data

SummaryObjective: The objective of this study was to undertake a proof of concept that demonstrated the use of primary care data and natural language processing and term extraction to assess emergency room use. The study extracted biopsychosocial concepts from primary care free text and related them to inappropriate emergency room use through the use of odds ratios.Methods: De-identified free text notes were extracted from a primary care clinic in Guelph, Ontario and analyzed with a software toolkit that incorporated General Architecture for Text Engineering (GATE) and MetaMap components for natural language processing and term extraction.Results: Over 10 million concepts were extracted from 13,836 patient records. Codes found in at least 1% percent of the sample were regressed against inappropriate emergency room use. 77 codes fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related concepts.Conclusions: Analyzed thematically, mental health issues and pain are important themes; we have concluded that pain and mental health problems are primary drivers for inappropriate emergency room use. Age and sex were not significant. This proof of concept demonstrates the feasibly of combining natural language processing and primary care data to analyze a system use question. As a first work it supports further research and could be applied to investigate other, more complex problems.

scholarly journals Natural Language Processing methods for Document Matching

2020 ◽  
Vol 6 (12) ◽  
pp. 379-383
Author(s):  
Maitri Patel and Dr Hemant D Vasava
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Original Work ◽  
Educational Systems ◽  
Processing Methods ◽  
Similar Test ◽  
Subject Examination ◽  
Different Types ◽  
Higher Educational

Data,Information or knoweldge,in this rapidly moving and growing world.we can find any kind of information on Internet.And this can be too useful,however for acedemic world too it is useful but along with it plagarism is highly in practice.Which makes orginality of work degrade and fraudly using someones original work and later not acknowleging them is becoming common.And some times teachers or professors could not identify the plagarised information provided.So higher educational systems nowadays use different types of tools to compare.Here we have an idea to match no of different documents like assignments of students to compare with each other to find out, did they copied each other’s work?Also an idea to compare ideal answeer sheet of particular subject examination to similar test sheets of students.Idea is to compare and on similarity basis we can rank them.Both approach is one kind and that is to compare documents.To identify plagarism there are many methods used already.So we could compare and develop them if needed.

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